A Machine-Learning-Algorithm-Assisted Intelligent System for Real-Time Wireless Respiratory Monitoring

Abstract

Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor—fabricated by the screen-printing method—is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. This work promotes the development of wearable health monitoring systems.

Compact, robust, and regulated-output hybrid generators for magnetic energy harvesting and self-powered sensing applications in power transmission lines

“…As the sliding continues, the next nylon film also acquires a positive charge through charge transfer (stage iv). By employing electrostatic induction, the charge redistributes through an external load to generate continuous AC output, 33–35 ultimately entering a stable state after a few cycles of saturation. The working principle of the stable state is demonstrated in Fig.…”

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

“…Artifacts can be effectively removed from EEG sensors using graph signal processing [31]. Motion artifacts can be reduced using analytical software tools [32], enhanced peak and valley detection algorithms [33], or through Fourier transformation [34].…”

Health Monitoring System from Pyralux Copper-Clad Laminate Film and Random Forest Algorithm

“…Then, the features are extracted from the time- and frequency-domain breathing samples, including signal amplitude, standard deviation, breathing cycle, mean value, variance, root mean square, and unbiased estimation. These features are used for the random forest model [ 37 , 38 ]. Finally, the classified respiration data are sent via Bluetooth to phones, tablets, or smartwatches.…”